2000 character limit reached
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies
Published 11 Mar 2024 in eess.SY, cs.LG, cs.SY, and stat.ML | (2403.06942v2)
Abstract: Purpose:This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. Leveraging recent progress in generative AI, machine learning, and networking technology, we develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages. Methods and Results:The proposed framework adopts the AI Foundation Model paradigm, where a generative and pre-trained (GPT) foundation model extracts physical features from power system measurements, enabling adaptation to a wide range of grid operation tasks. Replacing the LLMs used in popular AI foundation models, this approach is based on the Wiener-Kallianpur-Rosenblatt innovation model for power system time series, trained to capture the physical laws of power flows and sinusoidal characteristics of grid measurements. The pre-trained foundation model causally extracts sufficient statistics from grid measurement time series for various downstream applications, including anomaly detection, over-current protection, probabilistic forecasting, and data compression for streaming synchro-waveform data. Numerical simulations using field-collected data demonstrate significantly improved fault detection accuracy and detection speed. Conclusion:The future grid will be rich in inverter-based resources, making it highly dynamic, stochastic, and low inertia. This work underscores the limitations of existing Supervisory-Control-and-Data-Acquisition and Phasor-Measurement-Unit monitoring systems and advocates for AI-enabled monitoring and control with high-resolution synchro-waveform technology to provide accurate situational awareness, rapid response to faults, and robust network protection.
- A Day Without Power: Outage Costs for Businesses. https://www.bloomenergy.com/blog/a-day-without-power-outage-costs-for-businesses/. Accessed: 2024-03-08 Wiener [1958] Wiener, N.: Nonlinear Problems in Random Theory. M.I.T. paperback series. MIT Press, Cambridge, MA (1958). https://books.google.com/books?id=HQBRAAAAMAAJ Masani [1966] Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wiener, N.: Nonlinear Problems in Random Theory. M.I.T. paperback series. MIT Press, Cambridge, MA (1958). https://books.google.com/books?id=HQBRAAAAMAAJ Masani [1966] Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Wiener, N.: Nonlinear Problems in Random Theory. M.I.T. paperback series. MIT Press, Cambridge, MA (1958). https://books.google.com/books?id=HQBRAAAAMAAJ Masani [1966] Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Masani, P.: Wiener’s contributions to generalized harmonic analysis, prediction theory and filter theory. Bulletin of the American Mathematical Society 72(1.P2), 73–125 (1966) Wang et al. [2021] Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Wang, X., Liu, Y., Tong, L.: Adaptive subband compression for streaming of continuous point-on-wave and pmu data. IEEE Transactions on Power Systems 36(6), 5612–5621 (2021) https://doi.org/10.1109/TPWRS.2021.3072882 Silverstein and Follum [2020] Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Silverstein, A., Follum, J.: High-resolution, time-synchronized grid monitoring devices. Technical Report NASPI-2020-TR-004, North American Synchrophasor Initiative (2020). https://www.naspi.org/sites/default/files/reference_documents/pnnl_29770_naspi_hires_synch_grid_devices_20200320.pdf Anderson et al. [1999] Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Anderson, P.M., Agrawal, B.L., Van Ness, J.E.: Subsynchronous Resonance in Power Systems. John Wiley & Sons, New York (1999) Perez [2010] Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Perez, J.: A guide to digital fault recording event analysis. In: Proc. 63rd Annu. Conf. Protective Relay Engineers, pp. 1–17 (2010) Bastos et al. [2019] Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Bastos, A.F., Santoso, S., Freitas, W., Xu, W.: Synchrowaveform measurement units and applications. In: 2019 IEEE Power & Energy Society General Meetin G (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973736 Carroll [2019] Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Carroll, J.R.: A Practical Approach to Streaming Point-on-Wave Data. https://www.naspi.org/sites/default/files/2019-04/04_gpa_carroll_practical_approach_pow_20190417.pdf. Accessed: 2024–03-09 (2019) Rahmatian [2019] Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Rahmatian, F.: Point on Wave Measurements, Introduction. https://www.naspi.org/sites/default/files/2019-04/01_nugrid_rahmatian_pow_measurements_intro_20190416.pdf. Accessed: 2024–03-09 (2019) Izadi and Mohsenian-Rad [2020] Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Izadi, M., Mohsenian-Rad, H.: Event location identification in distribution networks using waveform measurement units. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, pp. 924–928 (2020). https://doi.org/10.1109/NAPS58826.2023.10318763 Xu et al. [2022] Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Xu, W., Huang, Z., Xie, X., Li, C.: Synchronized waveforms – a frontier of data-based power system and apparatus monitoring, protection, and control. IEEE Transactions on Power Delivery 37(1), 3–17 (2022) https://doi.org/10.1109/TPWRD.2021.3072889 Mohsenian-Rad and Xu [2023] Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Mohsenian-Rad, H., Xu, W.: Synchro-waveforms: A window to the future of power systems data analytics. IEEE Power and Energy Magazine 21(5), 68–77 (2023) Kailath [1970] Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Kailath, T.: The Innovations Approach to Detection and Estimation Theory. Proceedings of the IEEE 58(5), 680–695 (1970) https://doi.org/10.1109/PROC.1970.7723 Wang and Tong [2021] Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Wang, X., Tong, L.: Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection. arXiv:2106.12382 (2021). https://arxiv.org/abs/2106.12382 Sheskin [2011] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Editio (5th Ed.). Chapman and Hall/CRC, Boca Raton (2011). https://doi.org/10.1201/9780429186196 Sheskin [2007] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, ??? (2007) Ma and Perkins [2003] Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Ma, J., Perkins, S.: Time-series Novelty Detection Using One-class Support Vector Machines. In: Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3, pp. 1741–17453 (2003). https://doi.org/10.1109/IJCNN.2003.1223670 Dasgupta and Forrest [1995] Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data using Ideas from Immunology. In: In Proceedings of The International Conference on Intelligent Systems (1995) Gardner et al. [2006] Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-Class Novelty Detection for Seizure Analysis from Intracranial EEG. Journal of Machine Learning Research 7(37), 1025–1044 (2006) Schölkopf et al. [1999] Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support Vector Method for Novelty Detection. Proceedings of the 12th International Conference on Neural Information Processing Systems, 582–588 (1999) Khan and Madden [2014] Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Khan, S.S., Madden, M.G.: One-class Classification: Taxonomy of Study and Review of Techniques. The Knowledge Engineering Review 29(3), 345–374 (2014) https://doi.org/10.1017/S026988891300043X Bergmann et al. [2019] Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019) https://doi.org/10.5220/0007364503720380 Gong et al. [2019] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.: Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019) Lee et al. [2018] Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Lee, K., Lee, H., Lee, K., Shin, J.: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=ryiAv2xAZ Hendrycks et al. [2019] Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Hendrycks, D., Mazeika, M., Dietterich, T.: Deep Anomaly Detection with Outlier Exposure. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HyxCxhRcY7 Ren et al. [2019] Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Ren, J., Liu, P.J., Fertig, E., Snoek, J., Poplin, R., DePristo, M.A., Dillon, J.V., Lakshminarayanan, B.: Likelihood Ratios for Out-of-Distribution Detection. arXiv:1906.02845 (2019). https://arxiv.org/abs/1906.02845 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=Hkg4TI9xl Lakshminarayanan et al. [2017] Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf Liang et al. [2018] Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Liang, S., Li, Y., Srikant, R.: Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. (2018). Funding Information: The research reported here was supported by NSF Grant CPS ECCS 1739189. Publisher Copyright: © Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved.; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018 Lee et al. [2018] Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Lee, K., Lee, K., Lee, H., Shin, J.: A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc., ??? (2018). https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf Le Lan and Dinh [2021] Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Le Lan, C., Dinh, L.: Perfect density models cannot guarantee anomaly detection. Entropy 23(12) (2021) https://doi.org/10.3390/e23121690 Schlegl et al. [2019] Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30–44 (2019) https://doi.org/10.1016/j.media.2019.01.010 Dinh et al. [2015] Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Dinh, L., Krueger, D., Bengio, Y.: NICE: Non-linear Independent Components Estimation. arXiv:arXiv:1410.8516 (2015). https://arxiv.org/abs/1410.8516 Brakel and Bengio [2017] Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Brakel, P., Bengio, Y.: Learning Independent Features with Adversarial Nets for Non-linear ICA. arXiv:1710.05050 (2017) Sricharan and Hero [2011] Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Sricharan, K., Hero, A.: Efficient anomaly detection using bipartite k-nn graphs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc., ??? (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/8dd48d6a2e2cad213179a3992c0be53c-Paper.pdf Chen [2019] Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Chen, H.: Sequential change-point detection based on nearest neighbors. The Annals of Statistics 47(3), 1381–1407 (2019) https://doi.org/10.1214/18-AOS1718 Chen and Chu [2023] Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Chen, H., Chu, L.: Graph-based change-point analysis. Annual Review of Statistics and Its Application 10(1), 475–499 (2023) https://doi.org/10.1146/annurev-statistics-122121-033817 https://doi.org/10.1146/annurev-statistics-122121-033817 Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11106–11115 (2021) https://doi.org/10.1609/aaai.v35i12.17325 Zeng et al. [2023] Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’23/IAAI’23/EAAI’23. AAAI Press, ??? (2023). https://doi.org/10.1609/aaai.v37i9.26317 . https://doi.org/10.1609/aaai.v37i9.26317 Wang et al. [2024] Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Wang, X., Tong, L., Zhao, Q.: Generative Probabilistic Price Forecasting via Weak Innovations. Submitted for publications. See updated preprint at arXiv (2024) Mestav et al. [2023] Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Mestav, K.R., Wang, X., Tong, L.: A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems. IEEE Transactions on Power Systems 38(1), 4–13 (2023) https://doi.org/10.1109/TPWRS.2022.3168529 D’Agostino [2017] D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- D’Agostino, R.B.: Goodness-of-Fit-Techniques. (First Edition). Taylor and Francis, London (2017). https://www.taylorfrancis.com/books/9780203753064 Neyman [1937] Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Neyman, J.: ”smooth test” for goodness of fit. Scandinavian Actuarial Journal 1937(3-4), 149–199 (1937) https://doi.org/10.1080/03461238.1937.10404821 https://doi.org/10.1080/03461238.1937.10404821 Rayner and Best [1990] Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Rayner, J.C.W., Best, D.J.: Smooth tests of goodness of fit: An overview. International Statistical Review / Revue Internationale de Statistique 58(1), 9–17 (1990). Accessed 2024-01-18 Cover and Thomas [2006] Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Cover, T.M., Thomas, J.A.: Elements of Information Theory. (2nd Ed.). Wiley-Interscience, Hoboken, New Jersey (2006). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=2106155.274641.21607325&epcustomerid=s9001366 Gersho and Gray [1992] Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992). https://resolver.ebscohost.com/Redirect/PRL?EPPackageLocationID=7191.30710607.5628611&epcustomerid=s9001366 Telukunta et al. [2017] Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Telukunta, V., Pradhan, J., Agrawal, A., Singh, M., Srivani, S.G.: Protection Challenges under Bulk Penetration of Renewable Energy Resources in Power Systems: A Review. CSEE Journal of Power and Energy Systems 3(4), 365–379 (2017) https://doi.org/10.17775/CSEEJPES.2017.00030 Anderson [1999] Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Anderson, P.M.: Power System Protection. Mcgraw-Hill, New York (1999) El-Khattam and Sidhu [2008] El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- El-Khattam, W., Sidhu, T.S.: Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter. IEEE Transactions on Power Delivery 23(2), 576–585 (2008) https://doi.org/10.1109/TPWRD.2008.915778 Ibrahim et al. [2017] Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Ibrahim, D.K., Zahab, E., Mostafa, S.: New Coordination Approach to Minimize the Number of Re-adjusted Relays when Adding DGs in Interconnected Power Systems with a Minimum Value of Fault Current Limiter. International Journal of Electrical Power & Energy Systems 85, 32–41 (2017) https://doi.org/10.1016/j.ijepes.2016.08.003 Chattopadhyay et al. [1996] Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Chattopadhyay, B., Sachdev, M.S., Sidhu, T.S.: An On-line Relay Coordination Algorithm for Adaptive Protection using Linear Programming Technique. IEEE Transactions on Power Delivery 11(1), 165–173 (1996) https://doi.org/10.1109/61.484013 Liu et al. [2016] Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Liu, Z., Hoidalen, H.K., Saha, M.M.: An Intelligent Coordinated Protection and Control Strategy for Distribution Network with Wind Generation Integration. CSEE Journal of Power and Energy Systems 2(4), 23–30 (2016) https://doi.org/10.17775/CSEEJPES.2016.00045 Wan et al. [2010] Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Wan, H., Li, K.K., Wong, K.P.: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System. IEEE Transactions on Industry Applications 46(5), 2118–2124 (2010) https://doi.org/10.1109/TIA.2010.2059492 Papaspiliotopoulos et al. [2017] Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Papaspiliotopoulos, V.A., Korres, G.N., Kleftakis, V.A., Hatziargyriou, N.D.: Hardware-In-the-Loop Design and Optimal Setting of Adaptive Protection Schemes for Distribution Systems With Distributed Generation. IEEE Transactions on Power Delivery 32(1), 393–400 (2017) https://doi.org/10.1109/TPWRD.2015.2509784 Rezaei [2019] Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Rezaei, S.: Intelligent Overcurrent Protection During Ferroresonance in Smart Distribution Grid. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783752 Shen et al. [2017] Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Shen, S., Lin, D., Wang, H., Hu, P., Jiang, K., Lin, D., He, B.: An Adaptive Protection Scheme for Distribution Systems With DGs Based on Optimized Thevenin Equivalent Parameters Estimation. IEEE Transactions on Power Delivery 32(1), 411–419 (2017) https://doi.org/10.1109/TPWRD.2015.2506155 Ma et al. [2012] Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Ma, J., Wang, X., Zhang, Y., Yang, Q., Phadke, A.G.: A Novel Adaptive Current Protection Scheme for Distribution Systems with Distributed Generation. International Journal of Electrical Power & Energy Systems 43(1), 1460–1466 (2012) https://doi.org/10.1016/j.ijepes.2012.07.024 Liu et al. [2017] Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Liu, Z., Su, C., Høidalen, H.K., Chen, Z.: A Multiagent System-Based Protection and Control Scheme for Distribution System With Distributed-Generation Integration. IEEE Transactions on Power Delivery 32(1), 536–545 (2017) https://doi.org/10.1109/TPWRD.2016.2585579 Mahat et al. [2011] Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L.: A Simple Adaptive Overcurrent Protection of Distribution Systems With Distributed Generation. IEEE Transactions on Smart Grid 2(3), 428–437 (2011) https://doi.org/10.1109/TSG.2011.2149550 Fawzi et al. [2022] Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Fawzi, O., Flammarion, N., Garivier, A., Oufkir, A.: Sequential algorithms for testing identity and closeness of distributions (2022) [63] Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Ieee standard for inverse-time characteristics equations for overcurrent relays. IEEE Std C37.112-2018 (Revision of IEEE Std C37.112-1996), 1–25 (2019) https://doi.org/10.1109/IEEESTD.2019.8635630 Jain et al. [2019] Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705 Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
- Jain, R., Lubkeman, D.L., Lukic, S.M.: Dynamic adaptive protection for distribution systems in grid-connected and islanded modes. IEEE Transactions on Power Delivery 34(1), 281–289 (2019) https://doi.org/10.1109/TPWRD.2018.2884705
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.